Tag Archives: forest aesthetics

Using LiDAR data to assess forest aesthetics in McDonald-Dunn Forest

Bryan Begay

GEOG566

05/31/2019

  1. Question asked?

How does forest aesthetics vary depending on forest structure as a result of active management that retains vegetation and landforms?

In order to answer my question I would look at two stands in the McDonald-Dunn forest to do some analysis on how their forest structure is related to forest aesthetics. The first stand is Saddleback which had been logged in 1999 and shows signs of management. The second stand was identified near Baker Creek, and is 1 mile west of Saddleback. Baker Creek was chose for its riparian characteristics, as well as having no signs of management activates.

  1. A description of the data set.

The LiDAR data that I used with my initial analysis was 2008 DOGAMI LiDAR flown over the McDonald-Dunn forest. The RMSE for this data set was 1.5cm. The DEM data set I used was from 2009 has a RMSE of 0.3 m. A Canopy Height Model (CHM) was made in RStudio lidR package that used a digital surface model with a 1 meter resolution. The CHM was used to create an individual tree segmentation, where segmented trees were then converted to point data.

https://gimbalmonkey.github.io/SaddlebackPC/index.html

A link to a visualization of the raw point cloud that is georeferenced to its terrain.

  1. Hypotheses: predictions of patterns and processes you looked for.

I suspected that initially the Baker Creek stand would have higher forest aesthetic that would reflect in the stand’s unmanaged vegetation structure.

Rational

Since the Saddleback had been managed and cut I figured the more natural structure of the riparian stand would have generally higher forest aesthetics than a stand that has been altered by anthropogenic factors. Some processes that I hypothesized that relates to forest aesthetics to these stands was the spatial point pattern of trees could be related to forest aesthetics. Insert forest aesthetic link:

  1. Approaches: analysis approaches you used.

Exercise 1: Ripley’s K Analysis point pattern analysis

The steps taken to create a point pattern analysis was to identify individual trees and convert the trees into point data. The RStudio lidR package was used to create a Canopy Height Model and then an Individual tree segmentation. Rasters and shapefiles were create to export the data so I could then use the tree polygons to identify tree points. The spatstat  package was used in RStudio as well to perform a Ripley’s K analysis on the point data.

Figure 1. Individual Tree Segmentation using watershed algorithm on Saddleback stand.

Exercise 2: Geographically weighted Regression

The steps taken to do the geographically weighted regression included using the polyongs created from the individual tree segmentation to delineate tree centers. When tree points were created from the centroids of the polygons, which would be inputs for the GWR in ArcMap. A density raster and CHM raster had their data extracted to the point data so that density and tree height could be the variables used in the regression. Tree height was the explanatory variable and density was the independent variable.

Figure 2. Polygon output from Individual tree segmentation using the lidR package. The Watershed algorithm was the means of segmentation, and points were created from polygon centroids in ArcMap.

Exercise 3: Supervised Classification

This analysis involved creating a supervised classification by using training data from NAIP imagery and a maximum likelihood classification algorithm. It involved using the NIR band and creating a false color image that would show the difference spectral reflectance values from conifers and deciduous trees. I used a histogram stretch to visualize the imagery better and spent time gathering quality training data. I then created a confusion matrix by using accuracy points on the training data. I then clipped the thematic map outputs with my individual tree segmentation polygons to show how each tree had their pixels assigned.

  1. Results

The Ripley’s K analysis in ArcMap showed me that Saddleback stand’s trees are dispersed, and the Baker Creek stand’s trees were spatially clustered. GWR outputs told me that the model in the Saddle back stand showed me a map output where tree heights and density were positively related. The adjusted R2 was 0.69 and gave me a good output that showed me the tallest and densest trees were on the edges of Saddleback stand. The Baker Creek stand’s model performed poorly on the point data with an adjusted R2 of 0.5. The outputs only showed relationships could only be modeled on the upper left of the stand. The classified image worked well on Saddleback stand due to less distortion in the NAIP imagery on that stand, and the Baker Creek stand’s classification was not useful since it had significant distortion in the NAIP imagery.

Exercise 1:

Figure 3. ArcMap Ripley’s K function output for Saddleback stand assessing tree points.

Exercise 2:

Figure 4. Geographically weighted regression of Baker Creek and Saddleback stand. The Hotter colors indicate positive relationships between tree density and tree height.

Exercise 3.

Figure 5. Supervised image classification using a maximum likelihood algorithm on Saddleback stand.

  1. What did you learn from your results? How are these results important to science? to resource managers?

I learned that Ripley’s K outputs can differ depending on what packages used. R-studio Ripley’s K outputs told me that both my stands had clustered tree patterning. ArcMap outputs that made more sense told me that my Saddleback stand was actually dispersed. Outputs can be variable if inputs are not explicitly understood or modeled with enough care. I also learned that trying to model a very heterogeneous riparian stand is more difficult because of the variability. This is important for researchers who are interest in riparian areas like Baker Creek since they might need to have more variables to adequately model those stands.

  1. Your learning: what did you learn about software?

I became very familiar with processing and modelling with LiDAR point clouds. I also became familiar with Modelbuilder and learned how to use packages in R like Spatstat. I also found a new method for making a confusion matrix in ArcMap.

  1. What did you learn about statistics or other techniques?

I learned how to do point pattern analysis with Ripley’s K on tree points. This was done in R and in Arc. In Arc using the spatial statistics tool was also something I used and still plan to use. When using GWR I understood what it does, understood the outputs, and learned to properly interpret the results. I also became more concerned with issues of scale and networks that might affect my areas of interest.

Exercise 2: Geographically weighted regression on two forested stands.

Bryan Begay

  1. Initial Spatial Question: How does the spatial arrangement of trees relate to forest aesthetics in my areas of interest?

Context:

To understand forest aesthetics in my stand called Saddleback, I did a Ripley’s K analysis for Saddleback and on a riparian stand called Baker Creek to determine if the stands are clustered or dispersed.  The Baker Creek location is a mile west of the Saddleback stand.

  1. Geographically weighted Regression:

I performed a geographically weighted regression on both the Saddleback and the Baker Creek stands. The dependent variable was a density raster value and the explanatory value was tree height.

  1. Tools and Workflow

Figure 1. The workflow for creating the Geographically Weighted Regression for the Saddleback Stand. The Baker Creek stand followed the same workflow as well.

Results:

 

Figure 2. Geographically Weighted Regression showing the explanatory variable coefficients in the Saddleback and Baker Creek stands near Corvallis Oregon. Yellow color indicates negative relationships and the hotter colors  indicate positive relationships between tree height and density.

Figure 3. Geographically Weighted Regression showing the Local R2 values in the Saddleback and Baker Creek stands near Corvallis Oregon. Yellow color indicates that the local model is performing poorly, while hotter colors indicate better performance locally.

Table 1. Summary table output for the Saddleback stand’s geographically weighted regression.

Table 2. Summary table output for the Back Creek stand’s geographically weighted regression.

4. Interpretation/Discussion:

Having done the Ripley’s K analysis, I wanted to have a connection with this exercise, so I created a point density raster on both my stands (Figure 1). The point density raster calculates a magnitude-per-unit area from my tree points and outputs a density for the neighborhood around each tree point. The raster values would then be a descriptor of the trees neighborhood density. Having the density neighborhood values describes the stands tree spatial arraignment and relates to the Ripley’s K analysis outputs of telling if a stand is spatially clustered or dispersed.

Figure 2. shows that there is a spatial pattern in the Saddleback stand between density and height. There is a positive relationship on the edges of the stand and a decreasing relationship in the middle of stand between the two variables. This makes sense when thinking about how the stand would have denser and higher trees on the edges of the managed stand to screen the forest operations. The coefficient values for the baker creek showed a positive relationship on the north eastern portion of the stand, which would need further investigation to understand the relationship between density and height. Overall the relationship was negative in the Baker creek stand between density and height, but this may be attributed to the low local R2 values that indicate poor modeling (figure 3). Table 2. also shows that the Baker Creek model only accounted for 50% of the variance for the adjusted R2 values, which would indicate that more variables would be needed for the riparian stand. Figure 1. shows the summary table for GWR in the Saddleback stand.

  1. Critiques

The critiques for this exercise is that I only look at height and density. If I had more knowledge of working with LAS data sets I would have liked to have implemented the return values on the LiDAR data as an indicator of density. Another critique would be that I used density as a dependent variable and height as an explanatory variable. Using density as the dependent value allows me to see the spatial patterning of my trees when plotted in ArcMap so I can reference the Ripley’s K outputs for further analysis. Having height as a response variable with density as an explanatory is something that would have been easier for me understand and explain that relationship. Density can affect tree height in a stand but understanding tree height as a factor that affects density is not as intuitive. Looking at how tree height responds to density in my stand would tell something about tree height, but that relationship has already been explored in great depth.

A UAS and LiDAR based approach to maximizing forest aesthetics in a timber harvest

Bryan Begay

Research Question: 

Can LiDAR derived from an Unmanned Aerial System (UAS) create a point cloud driven visualization model for maximizing forest aesthetics in a highly visible timber harvest?

Context

A variable retention thinning is planned to be implemented in a harvest unit on the McDonald-Dunn Forest in a visible area near Corvallis. UAS systems offer an efficient way to collect data over large areas to create high quality data sets from LiDAR that can capture the structure of a forest stand. There is a need  for a model/methodology that utilizes UAS LiDAR point clouds to generate a visualization model to create a  timber harvest in an areas with high visibility that maximize forest aesthetics. Inputs for the model include DTMs, Google Earth Pro view shed tool, and point clouds. The point clouds can be manipulated to visualize an optimal silvicultural prescription that maximizes forest landscape aesthetics. Ancillary data of view shed and terrain from DTMs are inputs expected to help create a visualization model.

A description of the data set you will be analyzing, including the spatial and temporal resolution and extent: 

The data set I will be using will include high resolution LiDAR point clouds of a stand, Digital Terrain Models (DTM) from LiDAR point clouds flown by the USFS previously, and additional ancillary data from Google Earth Pro. The Google Earth Pro data will use the view shed tool for assessing the visual impact of regions in the harvesting unit. The spatial resolution will be using high resolution LiDAR point clouds on an area that is a few square kilometers. The temporal resolution will span data acquisition before the harvest, and then an assessment of the computer based prescription after harvest. The temporal resolution of the point cloud collected from the UAV will be collected in a discrete time frame of one day. The DTM data set and google earth pro data sets will be variable, but I anticipate them to be newer high resolution Google Earth imagery and high resolution LiDAR data sets.

Hypotheses:

I hypothesizes that LiDAR point clouds can be used in a visualization model to create a silvicultural prescription in a timber harvest that maximizes forest aesthetics in a logged area . Google Earth Pro view shed tool, high quality LiDAR point clouds, and a large body of literature on forest aesthetics provide a data set that is very rich in inputs to create a visualization model for timber harvests that maximizes forest aesthetics.

Approaches:

I would like to do some sort of analysis looking at the spatial relationship between forest aesthetics and timber harvests. A part of this analysis would look at the relationship of the spatial pattern of residual structure left from the thinning and the landscape aesthetics.

Expected outcome:

I would like an expected outcome to be a visualization model of the harvest unit that utilizes view shed and point clouds that maximizes forest aesthetics in a high viewership area.

Significance:

This spatial problem is important to the profession of forestry as well as other land managers, since it helps maintain the social license for foresters to practice forestry in areas that are highly visible. Public acceptance of harvesting practices is increased when forest aesthetics is taken into account, so creating a methodology and model to assist in creating silvicultural prescriptions that increase forest aesthetics is critical for public acceptance of forestry.

Level of preparation:

A. I have experience in ArcGIS.

B. No experience in modelbuilder and Python programming in GIS.

C. Some experience in R.

D. Experience in Digital Image Processing.

E. I’ve used Google Earth Engine and very little experience with MATLAB.